R-Store: A scalable distributed system for supporting real-time analytics
Bibliographic record
Abstract
It is widely recognized that OLTP and OLAP queries have different data access patterns, processing needs and requirements. Hence, the OLTP queries and OLAP queries are typically handled by two different systems, and the data are periodically extracted from the OLTP system, transformed and loaded into the OLAP system for data analysis. With the awareness of the ability of big data in providing enterprises useful insights from vast amounts of data, effective and timely decisions derived from real-time analytics are important. It is therefore desirable to provide real-time OLAP querying support, where OLAP queries read the latest data while OLTP queries create the new versions. In this paper, we propose R-Store, a scalable distributed system for supporting real-time OLAP by extending the MapReduce framework. We extend an open source distributed key/value system, HBase, as the underlying storage system that stores data cube and real-time data. When real-time data are updated, they are streamed to a streaming MapReduce, namely Hstreaming, for updating the cube on incremental basis. Based on the metadata stored in the storage system, either the data cube or OLTP database or both are used by the MapReduce jobs for OLAP queries. We propose techniques to efficiently scan the real-time data in the storage system, and design an adaptive algorithm to process the real-time query based on our proposed cost model. The main objectives are to ensure the freshness of answers and low processing latency. The experiments conducted on the TPC-H data set demonstrate the effectiveness and efficiency of our approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".